ORB-SLAM2安装与运行(非ROS环境下)
1.Prerequisites
1.1 C++11 or C++0x Compiler
We use the new thread and chrono functionalities of C++11.所以需要安装C++11编译器，运行：
$ sudo apt-get install gcc g++1.2 Pangolin
We use [Pangolin](https://github.com/stevenlovegrove/Pangolin) for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
$ sudo apt-get install libglew-dev #安装Glew
$ sudo apt-get install cmake #安装CMake
#安装Boost
$ sudo apt-get install libboost-dev libboost-thread-dev libboost-filesystem-dev
$ sudo apt-get install libpython2.7-dev #安装Python2 / Python3
#下载、编译、安装Pangolin:
$ git clone https://github.com/stevenlovegrove/Pangolin.git
$ cd Pangolin
$ mkdir build
$ cd build
$ cmake -DCPP11_NO_BOOST=1 ..
$ make
$ sudo make install 1.3 OpenCV（2.4.13）
We use [OpenCV](http://opencv.org) to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. **Required at leat 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2**.
$ sudo apt-get install build-essential libgtk2.0-dev libjpeg-dev libtiff4-dev libjasper-dev libopenexr-dev cmake python-dev python-numpy python-tk libtbb-dev libeigen2-dev yasm libfaac-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev libqt4-dev libqt4-opengl-dev sphinx-common texlive-latex-extra libv4l-dev libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev
$ unzip opencv-2.4.13.zip
$ cd opencv-2.4.13;mkdir build;cd build;
$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -DINSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -DBUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON -DCUDA_GENERATION=Kepler ..
$ make
$ sudo make install #在其中写入: /usr/local/lib
sudo gedit /etc/ld.so.conf.d/opencv.conf
#在文件末尾写入:
#PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
#export PKG_CONFIG_PATH
sudo ldconfig sudo gedit/etc/bash.bashrc
#source此脚本
source /etc/bash.bashrc 1.4 Eigen3
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. **Required at least 3.1.0**.注意:Opencv2.4.13编译时依赖此模块，最好在OpenCV2.4.13编译前安装
它能提供以下功能模块：
1) 密集矩阵和数组操作
2) 解密集线性方程组和矩阵分解
-求解线性最小二乘系统
-密集矩阵分解 (Cholesky, LU, QR, SVD, 特征值分解)
3) 解稀疏线性方程组和矩阵分解
-稀疏矩阵操作
-求解稀疏线性最小二乘系统
-稀疏矩阵分解（SpareCore, OrderingMethods, SpareCholesky, SpareLU, SparseQR,迭代线性求解）
4) 空间变换
- 2D旋转（角度）
- 3D旋转（角度+轴)
- 3D旋转(四元组: quaternion)
- N维缩放
- N维平移
- N维仿射变换
- N维线性变换（旋转、平移、缩放）
运行：
$ sudo apt-get install libeigen3-dev 1.5 BLAS and LAPACK
g2o需要BLAS和LAPACK
(1) BLAS: Basic Linear Algebra Subprograms
提供了基本的向量和矩阵操作：
- Level-1 BLAS: 支持 标量、向量、向量-向量 操作
- Level-2 BLAS: 支持 矩阵-向量 操作
- Level-3 BLAS: 支持 矩阵-矩阵 操作
(2) LAPACK：Linear Algebra PACKage
它调用BLAS来实现更高级的功能，支持以下操作：
- 解线性方程组
- 线性方程组的最小二乘解
- 特征值问题和奇异值问题
- 矩阵分解 (LU, Cholesky, QR, SVD, Schur, generalized Schur)
- 支持密集和带状矩阵，但不支持一般的稀疏矩阵
- 支持单精度和双精度
运行：
$ sudo apt-get install libblas-dev
$ sudo apt-get install liblapack-dev 1.6 DBoW2 and g2o (Included in Thirdparty folder)
We use modified versions of the [DBoW2](https://github.com/dorian3d/DBoW2) library to perform place recognition and [g2o](https://github.com/RainerKuemmerle/g2o) library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the *Thirdparty* folder.2 Building ORB-SLAM2 library and TUM/KITTI examples
2.1 编译ORB-SLAM2库
运行：
$ git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2
$ cd ORB_SLAM2
$ chmod +x build.sh
$ ./build.sh 2.2 运行TUM/KITTI例子程序
2.2.1 Monocular 实例
1）TUM 数据集
从http://vision.in.tum.de/data/datasets/rgbd-dataset/download下载并解压一个序列，如：rgbd_dataset_freiburg1_desk2.tgz
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER
运行上面的命令（把TUMX.yaml 修改为TUM1.yaml < freiburg1序列>,TUM2.yaml < freiburg2序列> or TUM3.yaml < freiburg3序列> ，PATH_TO_SEQUENCE_FOLDER是存放下载好的序列的目录）
#for example
$ cd ORB-SLAM2
$ ./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt ./Examples/Monocular/TUM1.yaml Dataset/TUM-dataset/rgbd_dataset_freiburg1_desk2
2） KITTI 数据集
从 http://www.cvlibs.net/datasets/kitti/eval_odometry.php下载数据集（灰度图像）， 把KITTIX.yaml 修改为 KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml，这些*xx.yaml各自对应于序列 0 to 2, 3, and 4 to 12. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.
#for example
$ ./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER2.2.2 Stereo 实例
1）KITTI 数据集
从 http://www.cvlibs.net/datasets/kitti/eval_odometry.php下载数据集（灰度图像），把KITTIX.yaml 修改为 KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml，这些*xx.yaml各自对应于序列 0 to 2, 3, and 4 to 12. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.
#for example
$ ./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER 2.2.3 RGB-D 实例
1）TUM 数据集
从http://vision.in.tum.de/data/datasets/rgbd-dataset/download下载并解压一个序列，如：rgbd_dataset_freiburg1_desk2.tgz
运行RGB-D实例时需要RGBD(depth)图像和RGB图像，所以需要把每一张RGB图像与之对应的RGBD图像建立关联（在Examples/RGB-D/associations/目录下有一部分关联文件，可以直接使用），关联Python文件associate.py(根据timestamp进行关联)。
#for example
$ python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt执行下面的命令（把TUMX.yaml 修改为TUM1.yaml < freiburg1序列>,TUM2.yaml < freiburg2序列> or TUM3.yaml < freiburg3序列> ），把 ASSOCIATIONS_FILE修改为对应的关联文件。
$./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE
# for example
$./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt ./Examples/RGB-D/TUM1.yaml ../tum-data/rgbd_dataset_freiburg1_desk2/ ./Examples/RGB-D/associations/fr1_desk2.txt